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An Energy Efficient Localization-Free Routing Protocol for Underwater Wireless Sensor Networks

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Recently, underwater wireless sensor networks (UWSNs) have attracted much research attention from both academia and industry, in order to explore the vast underwater environment. UWSNs have peculiar characteristics; that is, they have large propagation delay, high error rate, low bandwidth, and limited energy. Therefore, designing network/routing protocols for UWSNs is very challenging. Also, in UWSNs, improving the energy efficiency is one of the most important issues since the replacement of the batteries of underwater sensor nodes is very expensive due to the unpleasant underwater environment. In this paper, we therefore propose an energy efficient routing protocol, named (energy-efficient depth-based routing protocol) EEDBR for UWSNs. EEDBR utilizes the depth of sensor nodes for forwarding data packets. Furthermore, the residual energy of sensor nodes is also taken into account in order to improve the network lifetime. Based on the comprehensive simulation using NS2, we observe that EEDBR contributes to the performance improvements in terms of the network lifetime, energy consumption, and end-to-end delay. A previous version of this paper was accepted in AST-2011 conference.
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Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2012, Article ID 307246, 11 pages
doi:10.1155/2012/307246
Research Article
An Energy Efficient Localization-Free Routing Protocol for
Underwater Wireless Sensor Networks
Abdul Wahid and Dongkyun Kim
Graduate School of Electrical Eng i neering & Computer Science, Kyungpook National University, 1370 Sankyuk-Dong,
Buk-Gu Daegu, Republic of Korea
Correspondence should be addressed to Dongkyun Kim, dongkyun@knu.ac.kr
Received 4 November 2011; Accepted 1 February 2012
Academic Editor: Tai Hoon Kim
Copyright © 2012 A. Wahid and D. Kim. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Recently, underwater wireless sensor networks (UWSNs) have attracted much research attention from both academia and industry,
in order to explore the vast underwater environment. UWSNs have peculiar characteristics; that is, they have large propagation
delay, high error rate, low bandwidth, and limited energy. Therefore, designing network/routing protocols for UWSNs is very
challenging. Also, in UWSNs, i mproving the energy eciency is one of the most important issues since the replacement of the
batteries of underwater sensor nodes is very expensive due to the unpleasant underwater environment. In this paper, we therefore
propose an energy ecient routing protocol, named (energy-ecient depth-based routing protocol) EEDBR for UWSNs. EEDBR
utilizes the depth of sensor nodes for forwarding data packets. Furthermore, the residual energy of sensor nodes is also taken into
account in order to improve the network lifetime. Based on the comprehensive simulation using NS2, we observe that EEDBR
contributes to the performance improvements in terms of the network lifetime, energy consumption, and end-to-end delay. A
previous version of this paper was accepted in AST-2011 conference.
1. Introduction
The earth is a water planet, where more than 70% of its
area spans over water. Only less than 10% of the water
volumes (oceans) have been investigated, while a large area
still remains unexplored. The exploration of oceans is getting
more attention due to their usefulness such as presence
of natural resources, defense, and means of Corresponding
author: Dongkyun Kim transportation, and so forth. How-
ever, the traditional approaches for monitoring oceans have
many limitations such as high cost, longer time of accessing
the outcome of monitoring, and the unsuitable underwater
environment for human presence, and so forth. Hence,
underwater wireless sensor networks (UWSNs) are consid-
ered as important alternatives for exploring the oceans.
Recently, UWSNs have attracted much research attention
from both academia and industry, in order to explore the vast
underwater environment. UWSNs enable a large number of
applications such as environmental monitoring for scientific
exploration, disaster prevention, assisted navigation, and
oil/gas spills monitoring, and so forth.
Terrestrial sensor networks (i.e., ground-based sensor
networks) are well investigated, and many communication
protocols have been proposed for such networks. However,
UWSNs have dierent characteristics than the terrestrial
sensor networks. The major dierence is the employment
of the acoustic signals in UWSNs, in contrast to terrestrial
sensor network where the radio signals are used as a
communication media. The transition from the radio to the
acoustic signals is due to the poor performance of radio
signals in water. The radio signals propagate large distances
at extra low frequencies, requiring large antennas and high
transmission power.
The employment of acoustic signals as the communica-
tion media imposes many distinctive challenges on UWSNs.
In general, the UWSNs have the following intrinsic charac-
teristics. The acoustic signals have long propagation delay
(i.e., 1500 m/sec), that is, five orders of magnitude higher
than the radio signals used in terrestrial sensor networks.
The available bandwidth is limited due to attenuation and
high absorption factor of acoustic signals. The link quality
2 International Journal of Distributed Sensor Networks
is severely aected by the multipath fading and refrac t ive
properties of sound channels. Therefore, the bit error rates
are typically very high [1, 2].
Since the protocols proposed for terrestrial sensor net-
works are developed on the basis of the radio signals’ charac-
teristics such as low propagation delay and high bandwidth.
Therefore, they cannot be directly applied to UWSNs. Thus,
enormous eorts have been made for designing ecient
communication protocols while taking into account the
characteristics of the UWSNs.
The protocols proposed for UWSNs have addressed
various issues concerning the characteristics of the UWSNs.
Pa rticularly, improving the network lifetime is an important
issue in UWSNs since the replacement of the batteries of
underwater nodes is very expensiv e due to harsh underwater
environment. Therefore, the network protocol in UWSNs
should be designed considering the energy eciency to
improve the network life-time. The underwater sensor nodes
consume more energy in transmitting than receiving a
packet. Therefore, in order to reduce energy consumption,
consequently improving network life-time, the number of
transmissions needs to be reduced. In addition, one of the
important issues for improving the network lifetime is to
balance the energy consumption among the sensor nodes.
The workloa d should be equally divided among all the sensor
nodes over a path from a source towards a destination.
In this paper, we therefore propose an energy-ecient
depth-based routing protocol (named EEDBR) that per-
forms energy balancing and reduces the number of trans-
missions of sensor nodes in order to improve the network
life-time. In EEDBR, while forwarding a data packet from
a sensor node to a sink, the packet is transmitted by some
selected nodes. The selection of the nodes is based on
the depth and residual energy. The process is as follows.
Each sender broadcasts the data packet including a list
of its neighbors’ IDs, which contains only the IDs of the
neighbors having smaller depths than the sender. Hence, only
the selected neighboring nodes are al lowed to forward the
packet. Furthermore, EEDBR performs energy balancing by
utilizing the residual energy information of sensor nodes.
In EEDBR, sensor nodes hold the packet for a certain time
before forwarding. The holding time is based on the residual
energy of sensor nodes. A node having high residual energy
has a short holding time compared to the nodes having
low energy. Hence, the node with high residual energy
forwards the packet, and the low energy nodes suppress
their transmissions u pon overhearing the transmission of the
same packet. In this way, the energy balancing is achieved.
Due to the energy balancing, the sensor nodes consume their
energy parallely, and none of the sensor node’s battery is
exhausted earlier than others. Hence, the overall network
life-time is improved.
The rest of the paper is organized as follows. In Section 2,
we review some related routing protocols and their problems.
In Section 3, our proposed routing protocol, EEDBR, is
described in detail. Section 4 presents the performance
evaluation of EEDBR. Finally, conclusions are drawn in
Section 5.
2. Related Work
In this section, we present some related routing protocols
available in the literature. We take into account the well-
known routing protocols proposed for UWSNs. We divide
this section into two subsections: localization-based routing
protocols and localization-free routing protocols.
2.1. Localization-Based Routing Protocols. In this section,
some routing protocols which are based on the localization
of the sensor nodes are presented. In [3], a vector based
routing protocol called (vector based forwarding) VBF was
proposed. The data forwarding in VBF is as follows. A
source node, having a data packet to transmit, computes
a vector from itself towards the destination/sink node. The
source node then broadcasts the data packet including its
position/location information in the data packet. In VBF, the
nodes near the computed vector are used as relay nodes for
forwarding the data packet. Among all the receiving nodes
of a broadcast from a sender, only the nodes located in a
predefined radius around the computed vector participate
in the forwarding of the data packet. The employment of
the predefined radius allows a reduced number of nodes
to forward the data packet. Hence, the proposed scheme
employs the concept of controlled flooding in the network.
However, the limitation of the proposed scheme lies in
requiring the localization of sensor nodes, which itself is
a crucial issue in UWSNs. Furthermore, in case of sparse
networks, the unavailability of sensor nodes in the predefined
radius aects the performance.
In [4], a routing protocol called (hop by hop vector based
forwarding) HHVBF was proposed. HHVBF is the successor
of VBF, where the vector is computed on per hop basis. In
HHVBF, due to the computation of the vector on per hop
basis, performance improvements are achieved over VBF.
However, HHVBF still requires the localization of sensor
nodes, which l imits the applicability of the proposed scheme
in real environment.
In [5], a routing protocol called FBR (Focused Beam
Routing) protocol was proposed. FBR is a cross-layer
approach where dierent transmission power levels are used
during the forwarding of the data packet. The sender of
the data packet transmits an RTS packet with a certain
transmission power level. If a CTS reply is received from a
relay node residing closer to the sink node, the data packet is
transmitted to that relay node. Otherwise, the transmission
power level is increased to a higher level. FBR uses a range
of transmission power levels for example, P
1
to P
N
.The
limitation of the FBR protocol lies in the assumption that
the source node knows its own location and the location of
the destination/sink node. Furthermore, the use of RTS/CTS
during the forwarding of the data packets causes increased
delay and excessive energy consumption. In [6],arouting
protocol called (directional flooding-based routing) DFR
was proposed. DFR is another routing protocol with the
assumption of the localization of sensor nodes.
In [7], (sector based routing with destination location
prediction) SBR-DLP was proposed. In SBR-DLP, it is
assumed that a mobile sink is available in the network and
International Journal of Distributed Sensor Networks 3
n1
n2
n3
n4
S
Figure 1: Scenario illustrating drawback of DBR.
that each sensor node is aware of the movement schedule
of the mobile sink. The data forwarding process is as
follows. A source node broadcasts a chk
ngb packet. The
neighboring nodes reply with a chk
ngb reply packet. The
chk
ngb reply packet contains the sector number of the
neighboring nodes. The sectors are computed based on the
distance from the vector (i.e., the vector between the source
and the sink node). Upon receiving chk
ngb reply packets
from its neighbors, the source node assigns priorities to the
neighboring nodes based on the sector number. Then, the
neighboring node closest to the mobile sink is selected as
a forwarder. The limitations of the proposed scheme are
in requiring a localization technique, a large delay due to
chk
ngb/chk ngb reply packets and the hard assumptions.
2.2. Localization-Free Routing Protocols. In this section, we
present some localization-free routing protocols for UWSNs.
In [8], a localization-free routing protocol called (depth
based routing) DBR was proposed. DBR uses the depth of
the sensor nodes as a metric for forwarding data packets.
During data forwarding, the sender includes its depth in
the data packet. The receiving nodes compare their depths
to the depth of the sender. The node having smal ler depth
participates in forwarding the data packet. Each node has a
certain holding time for each data packet, where the nodes
having smaller depths have a short holding time compared
to the nodes having higher depths. Since only the depth of
sensor nodes is used as a metric for forwarding, most of
the time, the nodes having smaller depths are involved in
forwarding. Hence, such nodes die earlier than the other
nodes in the network, which creates the routing holes in the
network. Such a scenario is illustrated using Figure 1.
In Figure 1, node S is the sender of the packet, and nodes
n1, n2, n3, and n4 are the receiving nodes. According to the
approach employed by DBR, nodes n1, n2, and n3 are eligible
for forwarding the packet because of having smaller depths
than the sending node S. However, every time, the packet is
forwarded by node n1 since node n1 has lower depth than
the nodes n1 and n2, therefore, having short holding time.
Consequently, due to the frequent forwarding, node n 1 will
die earlier than node n2 and node n3, which will create a
routing hole in the network. Since all the nodes have the same
approach of forwarding the data packets, routing holes are
created all over the network. Due to the routing holes, the
network is partitioned into parts which aect the network
lifetime.
Furthermore, in DBR, with the increase in network den-
sity, the number of redundant transmissions also increases
because the probability of small dierence among nodes’
depths also increases with the network density and the
nodes having similar depths also have similar holding
times. Due to the long propagation delay in underwater
environment, before overhearing the same packet from a
sender, a node’s timer expires. Consequently, that node also
transmits the packet. Hence, all the nodes which are having
small dierences among their holding times will transmit the
packet before overhearing the same packet from other nodes.
Thus, a lot of redundant packets will be transmitted, leading
to excessive energy consumption.
In [9], H
2
-DAB (hop-by-hop dynamic addressing based)
routing protocol was proposed. H
2
-DAB assigns a unique
address (called HopID) to each sensor node based on the
hop count from the sink node. The process is as follows. The
sink node broadcasts a Hello packet. Each receiving node
is assigned a HopID. Then, the receiving nodes increment
the HopID and rebroadcast the Hello packet including the
updated HopID. Since the HopID is increased hop by hop,
the sensor nodes closer to the sink will be assigned smaller
HopIDs than the nodes away from the sink. During the
forwarding of data packets, the nodes having small HopIDs
are selected for forwarding the data packets. Similar to DBR,
the nodes h aving small HopIDs are frequently used for
forwarding the data packets. Hence, these nodes having small
HopIDs die earlier than the other nodes in the network. In
addition, only hop count-based metric is not suitable in a
resource-constrained network as UWSN. Furthermore, H
2
-
DAB uses inquiry request and inquiry reply packets during the
forwarding of the data packets, which is expensive in terms of
delay and energy.
In [10], Winston et al. proposed a virtual sink architec-
ture where multiple sinks are assumed connected to each
other. In the proposed scheme, each sink broadcasts a Hello
packet (called hop count update packet). Upon receiving
the Hello packet, each sensor node is assigned a hop count
value. During the forwarding of the data packets from a
source towards a sink node, these hop count values are used
in the selection of a next forwarding node. The proposed
schemes limitations are the redundant transmissions (i.e.,
the t ransmission of the same packet towards multiple sinks)
and the hard assumption of the connectivity among the sink
nodes.
In [11], a network protocol called (multipath power-
control transmission) MPT was proposed. MPT uses a cross
layer approach, it combines power control with multi-path
routing. The proposed scheme is divided into three phases:
multipath routing, source-initiated powe r-control t ransmission
and destination node’s packet combining. Initially, the source
node transmits a route request packet, the destination
node reply with multiple route reply packets. Since the
4 International Journal of Distributed Sensor Networks
route request is broadcasted, therefore, the route request
packet follows multiple paths towards the destination. Con-
sequently, multiple route reply packets, following dierent
paths, are received by the source node. Then an optimum
number of paths are selected based on the path length and the
number of paths. Source node also computes optimal energy
distribution along a path based on the collected information
(i.e., number of paths, number of hops in each path, perhop
distance) during path establishment phase. The optimal
energy distribution information is also included in the data
packet, and based on this information each forwarder selects
its transmission power. Finally, upon receiving the data
packet, the destination node combines multiple erroneous
copies (since some packets might be corrupted) of a packet
received from multiple paths into a single copy to recover
the original packet. The limitations of the proposed scheme
are the redundant packets’ transmissions and probabilistic
approach used in the computation of optimal energy distri-
bution.
In this paper, similar to DBR, we also employ the depth
of sensor nodes for the selection of the forwarding nodes.
However,ourproposedschemeisdierent from DBR as
follows.
(1) DBR uses only the depth of sensor nodes with-
out taking into account the residual energy of
the sensor nodes. In addition, in DBR, there is
no method/approach for energy balancing among
sensor nodes. In contrast, in our proposed scheme,
the energy balancing of sensor nodes is employed in
order to improve the network life-time.
(2) In DBR, the number of forwarding nodes increases
as the network density increases. However, in our
proposed scheme, the number of forwarding nodes is
restricted on the basis of not only the depth but also
the residual energy of the sensor nodes.
(3) DBR is a receiver-based approach, where the receiv-
ing nodes decide whether to forward the received
data packet or not. There is a high probabil-
ity of redundant transmissions in a receiver-based
approach due to the lack of neighboring nodes’
information such as depth and residual energy. In
contrast, our scheme is a sender-based approach
where the sender decides the forwarding nodes based
on the neighboring nodes’ depths and residual energy
information. Hence, the sender can select a limited
number of suitable forwarding nodes.
3. Our Proposed Protocol: Energy Efficient
Depth-Based Routing Protocol (EEDBR)
In this section, we introduce our proposed routing protocol,
EEDBR, in detail. EEDBR consists of two phases: knowledge
acquisition phase and data forwarding phase. During the
knowledge acquisition phase, sensor nodes share their depth
and residual energy information among their neighbors. In
the data forwarding phase, data packets are transmitted from
the sensor nodes to the sink node.
We have divided this section into three subsections:
network architecture, knowledge acquisition phase and data
forwarding phase.
3.1. Network Architecture. Figure 2 shows the architecture
of UWSN. Multiple sink nodes are deployed on the water
surface, and the sensor nodes are deployed underwater from
the top to the bottom of the deployment region. It is assumed
that the sink nodes are equipped with the acoustic and
radio modems. These sink nodes use acoustic modems, for
communication with the underwater sensor nodes, and the
radio modems, for communication with other sinks or an
onshore data center. Since the radio communication is much
faster than the acoustic communication, the data packet once
received at any sink is considered delivered to all sinks and the
onshore data center.
3.2. Knowledge Acquisition Phase. During this phase, the sen-
sor nodes share their depth and residual energy information
among their neighbors. The purpose of this sharing is to
allow the sensor nodes to select the most suitable neighbors
as forwarders during the data forwarding phase. When a
sensor node has a data packet to send to the sink node, the
depth and the residual energy information are used in the
selection of forwarding nodes. In this knowledge acquisition
phase, knowledge means the depth and residual energy of a
sensor node.
The knowledge acquisition process is as follows. Each
sensor node broadcasts a Hello packet to its one hop
neighbors. The Hello packet contains the depth and the
residual energy of the broadcasting node. The format of
the Hello packet is shown in Figure 3. Upon receiving
the Hello packet, the neighboring nodes store the depth
and the residual energy information of those sensor nodes
having smaller depth. The neighboring nodes only store the
information about the sensor nodes having smaller depths
since it is obv ious that the data packets are transmitted
towards the sink nodes residing on the water surface. Hence,
storing the depth and residual energy information of all the
neighboring nodes i s not required, which lessens the burden
of storing a large number of data.
It is reported that, in UWSNs, the sensor nodes reside at
the same depth. This is because the sensor nodes move with
water currents in horizontal direction, and the movements in
vertical direction are almost negligible [4]. Hence, the updat-
ing of the depth information is not significant. However, the
residual energy of the sensor nodes changes over time due
to the dierent operations, that is, transmitting, receiving,
processing, and idle listening. Therefore, the residual energy
information of the sensor nodes needs to be updated. For this
purpose, a distributed approach is employed in our proposed
scheme. Each sensor node checks its residual energy on
an interval basis. If the dierence between the current and
previous residual energy of a sensor node is larger than
a threshold (i.e., a system parameter), that sensor node
broadcasts the Hello packet including the updated residual
energy to its one-hop neighbors. In this way, the residual
energy information of the sensor nodes is updated among the
International Journal of Distributed Sensor Networks 5
Wat er
surface
Sink node
Sensor node
Onshore data center
Radio link
Figure 2: Architecture of an underwater wirless sensor network.
Sender
ID
Residual
energy
Depth
Figure 3: Format of the hello packet.
neighboring nodes. Furthermore, the knowledge acquisition
phase is executed on an interval basis. This is done to update
the sensor nodes about their most recent neighboring nodes
and their updated residual energy and depths. However, the
interval of knowledge acquisition phase is set long in order to
avoid the overhead due to the broadcasts of the Hello packets.
Hence, there is a tradeo between the overhead and having
the updated information about the neighboring nodes.
3.3. Data Forwarding Phase. During this phase, the data
packets are forwarded from a source node towards a desti-
nation/sink node on the basis of the depth and the residual
energy information of the sensor nodes. The information
about the depth of the sensor nodes allows the selection of
those forwarding nodes which are closer to the sink than
the sender of the data packet. In addition, the residual
energy information about the sensor nodes is used to select
the node having high residual energy among its neighbors.
The selection of the node having high energy attempts to
balance the energy consumption among the sensor nodes. In
EEDBR, since each sensor node has the information about
its neighbors’ depth and the residual energy, a sending node
can select the most suitable next hop forwarding nodes.
Therefore, the sending node selects a set of forwarding nodes
among its neighbors having smaller depth than itself. The set
of forw arding nodes is included as a list of IDs in the data
packet.
Upon receiving the data packet, the forwarding nodes
hold the packet for a certain time based on their residual
energy. The sensor node having more residual energy has a
short holding time. The holding time (T) is computed using
(1).
T
=
1
current energy/initial energy

max holding time + p,
(1)
where max
holding time is a system parameter (i.e., the
maximum holding time a node can hold a packet), and p
is the priority value.
The priority value is used to prevent multiple forwarding
nodes from having the same holding time since the sensor
nodes might have the same residual energy level. Therefore,
if the holding time is only based on residual energy, the nodes
having same residual energy will also have the same holding
time. In such a case, the forwarding nodes will forward the
packet at the same time. Hence, redundant packets will be
transmitted. In order to avoid such redundant transmissions,
the priority value is added to the holding time in order
to make the dierence among the holding times of the
forwarding nodes having the same residual energy.
The priority value is computed as follows. The sending
node sorts the forwarding list on the basis of the residual
energy of the forwarding nodes. Upon receiving the data
packet, the forwarding nodes add the priority value to the
holding time based on their position in the list. The priority
value is initialized with a starting value, and the priority value
is doubled with the increase in the position index of the
nodes in the list. Hence, due to the dierent positions in the
list, the nodes have dierent priority values. Consequently,
the nodes having the same residual energy will have dierent
holding times even for the same packet.
Figure 4(e) illustrates the scenario where the forwarding
nodes have same residual energy, and node S is the sender of
6 International Journal of Distributed Sensor Networks
90
80
A
B
S
Same depth, dierent energy
(a)
90
90
A
B
S
Dierent depth, same energy
(b)
90
80
A
B
S
Dierent depth, dierent energy
(c)
90
80
A
B
S
Dierent depth, dierent energy
(d)
90
90
S
Same depth, same energy
A
B
(e)
Figure 4: Dierent possible scenarios during the forwarding of the data packet.
International Journal of Distributed Sensor Networks 7
the packet, and node, A and B are the candidate forwarding
nodes. The value 90 is assumed as the residual energy of
the nodes A and B. As illustrated in Figure 4(e),bothnodes
A and B have the same residual energy. When these nodes
receive the packet, they check their position in the forwarding
list. On the basis of the position in the list, both the nodes
compute the priority value. Let’s assume that the nodes A
and B are positioned at the second and third positions in
the list and assume that the priority value is started with a
starting value 10. Then, the priority value of node A will
become 20, and node B will have the priority value of 40,
because the priority value is doubled corresponding to the
position in the list. Hence, despite of having same residual
energy, b oth the nodes have dierent holding times for
the same packet. Furthermore, since the dierence between
the holding times of both the nodes is double, a node has
an enough holding time for overhearing the same packet
from other sensor nodes. In contrast, in DBR, the dierence
between the holding times of the sensor nodes having similar
depths is not long enough for overhearing . Hence, redundant
packet transmissions are unavoidable in DBR. In EEDBR,
the topmost node in the list has the highest pr iority because
of having the highest residual energy among its neighbors.
Therefore, we employ a holding time of zero for the topmost
node in the list in order to reduce the end-to-end delay.
The topmost node will forward the data packet as soon as
it receives the data packet.
During the forwarding of the data packet on the basis of
the depth and residual energy, dierent scenarios are possible
as shown in Figure 4 where node S is the sender, nodes A
and B are the forwarding nodes, and the values 90 and 80 are
assumed as the residual energy values of A and B. Here, we
describe how EEDBR responds to such scenarios. In case (a),
both the nodes are having same depth. However, the sensor
node A forwards the packet since it has more energy than
node B. In case (b), both nodes have same residual energy.
However, node B forwards the packet because it is located at
the lower depth than node A. Similarly, in case (3), node B
forwards the packet since it has more energy and also it is
located at the lower depth. In case (d), node A forwards the
packet because of having more energy. Here, it is also possible
to give a priority to the node wh ich has lower depth, which
means it is nearer to the sink node. However, for the energy
balancing purpose, the node having more energy is preferred.
Finally, in case (e), since both the nodes have the same depth
and the same residual energy, anyone can be selected for
forwarding. As above-mentioned, both the nodes will have
dierent holding times. Hence, one node will transmit the
packet, and the other will suppress its transmission upon
overhearing the transmission of the same packet.
In UWSNs, the suppressions of packet transmissions
contribute to reducing the energy consumption, hence,
improving energy eciency. However, too much suppression
of packet transmissions aects the delivery ratio. In some
applications such as military surveillance, the delivery ratio is
more important than the energy eciency. Hence, in order to
support such applications, we employ an application-based
suppression scheme. In our suppression scheme, when the
delivery ratio is less than a given delivery ratio threshold, the
number of nodes which suppress their packet transmissions
is reduced in order to meet the desired delivery ratio. During
the forwarding of the data packets, the source includes the
number of packets generated by that source. Upon receiving
the data packets, the sink node computes the delivery ratio
by dividing the number of data packets received at the sink
to the number of data packets generated by the source node.
If the delivery ratio is less than the desired delivery ratio
based on the application requirement, the sink node informs
the source node by sending/flooding a packet containing the
delivery ratio at the sink. Consequently, the source node
includes the delivery ratio value received from the sink
into the data packet. Upon receiving the data packet, the
forwarding node decides whether to suppress or transmit the
packet based on the deliver y ratio value in the data packet.
Here, a probabilistic a pproach is used. The forwarding nodes
generate a random number. If the random number is less
than the delivery ratio value, the packet is transmitted
without any suppression even if the same packet is received
from other nodes. In this way, the degree of suppressions of
packet transmissions is controlled. Hence, there is a tradeo
between the energy eciency and the delivery ratio, and the
proposed scheme, EEDBR, can be switched interchangeably
based on the application requirement.
In EEDBR, the data packets forwarding from a source
node to a sink is summarized as follows. Each sender of
the data packet includes a list of its neighboring nodes
having smaller depths, called forwarding nodes. The list is
ordered on the basis of the residual energy values of the
forwarding nodes. Upon receiving the data packet, the first
node in the list forwards the data packet immediately without
waiting. The rest of the forwarding nodes in the list holds the
data packet for a certain time computed using (1). Dur ing
the holding time, upon overhearing the same data packet
from another sensor node, the forwarding nodes generate
a random number and compare it to the delivery ratio
value received in the data packet. The nodes suppress the
transmission if the random number is less than the delivery
ratio. Otherwise, the data packet is transmitted. In case where
no data packet is overheard during the holding time, the
data packet is transmitted when the holding time expires. To
illustrate further, the operation during the forwarding of the
data packet is shown in Figure 5.
4. Performance Evaluation
In this section, we evaluate the performance of our proposed
routing protocol, EEDBR, by comparing it to an existing
routing protocol in UWSNs called DBR [8]. Since DBR is a
representative localization-free routing protocol in UWSNs,
we select DBR for the performance comparison.
4.1. Simulation Se ttings. Simulations were conducted using
a commonly used network simulator called NS-2. We
performed simulations with a dierent number of sensor
nodes (i.e., 25, 49, 100, and 225). We employed grid a nd
random topologies for the comparisons. In each topology,
the transmission range of 250 meters was set for each sensor
8 International Journal of Distributed Sensor Networks
Packet received
Drop the packet
No
Yes
Compute holding time
Yes
Yes
Drop the packet
Transmit the packet
Transmit the packet
No
No
Check if ID
is present in
the list
The ID is
positioned at
the first position
in the list
Same packet is
received during
holding time
Generate a
random number
compare it to
delivery ratio
Random number <
delivery ratio
Random number >
delivery ratio
Transmit the packet
when the timer expires
Figure 5: Operation at the forwarding node.
node. The initial energy value of 70 joule was set for all the
sensor nodes. Dierent numbers of sink nodes were used for
each topology (i.e., 2, 3, 4, and 6 sink nodes for 25, 49, 100
and 225 sensor nodes). In each topology, two source nodes
were randomly selected from the bottom of the deployment
region. Each source node generated a data packet of a size of
64 bytes every 15 seconds. The 802.11-DYNAV [12]protocol
was used as an underlying MAC protocol. For all topologies,
the results were averaged from 30 runs.
4.2. Performance Metrics. We used the following metrics
for evaluating the performance of our proposed routing
protocol.
Network Lifetime. Network life-time is the time when the
first node dies in the network when the energy of that node
is fully exhausted.
Energy Consumption. Energy consumption is evaluated
through the total amount of energy consumed by the sensor
nodes during the forwarding of the data packets from a
source towards a destination/sink node.
End-to-End Delay. The end-to-end delay is the time taken
by a packet to reach from a source node to a destination/sink
node.
Delivery Ratio. Delivery ratio is defined as the ratio of the
number of packets successfully received at the sink node to
the number of packets transmitted from the source node.
1400
1200
1000
800
600
400
200
0
25 49 100 225
EEDBR
DBR
Number of nodes
Network life-time (s)
Figure 6: Comparison of network lifetime in random topology.
4.3. Simulation Results and Analysis
4.3.1. Network Lifetime. The network life-time of both the
schemes in random and grid topologies is compared as
shown in Figures 6 and 10, respectively. EEDBR shows
improved performance over DBR. Since DBR selects the
nodes having smaller depths to be frequently used for
forwarding the data packets. Therefore, the energy of such
nodes is exhausted rapidly, and these nodes die very soon. In
International Journal of Distributed Sensor Networks 9
120
100
80
60
40
20
0
25 49 100 225
EEDBR
DBR
Number of nodes
Energy consumption (J)
Figure 7: Comparison of energy consumption in random topology.
contrast, EEDBR employs the energy balancing among the
sensor nodes in order to enable the sensor nodes to consume
their energy parallely. Hence, the sensor nodes stay alive
for long time. Furthermore, the lower energy consumption
is another factor of the improved network life-time in the
proposed EEDBR scheme. In EEDBR, a limited number of
sensor nodes are allowed to participate in forwarding . The
forwarding is not only restricted on the basis of the depth but
also the residual energy of the sensor nodes. In addition, DBR
cannot avoid redundant packet transmissions. The sensor
nodes having similar depths also have similar holding times.
Therefore, the same packets are transmitted at the same time
in DBR. In contrast, in EEDBR, due to the employment of
the priority values, the redundant transmissions of packets
do not occur. Hence, the reduction in energy consumptions
is achieved, which also improves the battery life-time of the
sensor nodes.
4.3.2. Energy Consumption. Figures 7 and 11 shows, the
energy consumption of both the schemes in random and
grid topologies, respectively. The energy consumption of
DBR is hig her than the proposed EEDBR protocol due to
excessive number of nodes’ involvement in forwarding the
data packet and redundant packets transmissions in DBR
as mentioned earlier. As shown in the figures, the energy
consumption of both the schemes is increasing with the
increase in network density. This is because more nodes
become eligible for forwarding the data packet with the
increase in network density. However, DBR only restricts the
number of nodes on the basis of the depth of the sensor
nodes. Only utilizing the depth of the sensor nodes can not
reduce the number of nodes since sensor nodes have similar
depths. In contrast, EEDBR restricts the number of nodes,
based on two metrics: the depth and the residual energy.
Furthermore, in EEDBR, due to the priority assignment
technique, nodes have enough dierence in their holding
times. Therefore, the nodes holding a packet suppress their
70
60
50
40
30
20
10
0
25 49 100 225
EEDBR
DBR
Number of nodes
End-to-end delay (s)
Figure 8: Comparison of end-to-end delay in random topology.
transmissions upon overhearing the transmission of the
same packet form a high priority sensor node.
4.3.3. End-to-End Delay. The end-to-end delay of both the
schemes is investigated as shown in Figures 8 and 12 in
random and grid topology, respectively. In DBR, each sensor
node holds the packet for a certain time proportional to the
depth of the sensor node. Therefore, DBR has a long end-
to-end delay. In contrast, EEDBR wants the first node in the
list of forwarding nodes to transmit the packet as soon as it
receives the packet. Therefore, the delay is reduced only to the
propagation delay of the packet. As depicted in the figures
in both random and grid topologies, the delay in DBR is
continuously increasing with the increase in network density
because the number of forwarding nodes also increases with
the increase in network density. Since each node holds the
packet for a certain time, the overall holding time of the
packet also increases. The increase in network density does
not aect the end-to-end delay in EEDBR, because each time
the first forwarding node in the list has a holding time of zero.
4.3.4. Delivery Ratio. Figures 9 and 13 show the delivery
ratio of both the schemes in random and grid topologies,
respectively. The delivery ratio is much better in random
topology than the grid topology, where the delivery ratio is
higher than 94% for both the schemes in random topology.
The delivery ratio is more than 90% in grid topolog y.
Howe ver, when the network density reaches 225 nodes, the
deliver y ratio is abruptly dropped to 85%, since the number
of collisions also increases with the increase in number of
nodes. Relatively, DBR has better deliver y ratio than EEDBR.
The delivery ratio of DBR is 2 to 3% higher than EEDBR
in random topology and 5 to 7% higher in grid topology.
This is because DBR makes packets transmitted redundantly
where multiple paths are followed to reach the sink node.
Hence, the delivery ratio is high in DBR. However, the high
10 International Journal of Distributed Sensor Networks
100
80
60
40
20
0
25
49
100
225
EEDBR
DBR
Number of nodes
Delivery ratio (%)
Figure 9: Comparison of delivery ratio in random topology.
1400
1200
1000
800
600
400
200
0
25
49
100
225
EEDBR
DBR
Number of nodes
Network life-time (s)
Figure 10: Comparison of network lifetime in grid topology.
deliver y ratio in DBR is with the expense of excessive energy
consumption and increased end-to-end delay.
5. Conclusions
Improving the energy eciency in underwater wireless sen-
sor networks (UWSNs) is one of the important issues, since
the replacement of the batteries of underwater sensor nodes
is very expensive due to harsh underwater environment. In
this paper, we therefore proposed an energy ecient depth-
based routing protocol (named EEDBR) for UWSNs. EEDBR
utilizes the depth and the residual energy of sensor nodes
as a routing metric. In particular, EEDBR does not require
the localization of the sensor nodes which itself is a crucial
issue in UWSNs. EEDBR employs a sender-based approach
70
60
50
40
30
20
10
0
25
49 100
225
EEDBR
DBR
Number of nodes
Energy consumption (J)
Figure 11: Comparison of energy consumption in grid topology.
70
60
50
40
30
20
10
0
25 49 100 225
EEDBR
DBR
Number of nodes
End-to-end delay (s)
Figure 12: Comparison of end-to-end delay in grid topology.
for routing where the sender decides a set of next for-
warding nodes in order to reduce redundant transmissions
from multiple for warders. EEDBR has two phases, namely,
knowledge acquisition phase and data forwarding phase. In
the knowledge acquisition phase, each sensor node shares its
depth a nd residual energy with its neighbors through Hello
messages. In the data forwarding phase, each sender of the
data packet includes a list of its neighboring nodes to the data
packet. The set of the neighboring nodes called forwarding
set/list is selected based on the depth of the neighboring
nodes. Upon receiving the data packet, the forwarding nodes
hold the packet for a certain t ime. The holding time is based
on the residual energy of the forwarding nodes. Furthermore,
we employed a novel suppression technique for the nodes
overhearing the same packet. The degree of suppression of
packet transmissions is controlled based on the delivery r atio
which is notified by the sink node.
International Journal of Distributed Sensor Networks 11
100
80
60
40
20
0
25
49 100 225
EEDBR
DBR
Number of nodes
Delivery ratio (%)
Figure 13: Comparison of delivery ratio in grid topology.
Through NS-2 network simulations, the EEDBR pro-
tocol was compared to a representative routing protocol
in UWSNs called DBR [8]. Based on the comprehensive
simulation, we observed that EEDBR contributes to the
performance improvements in terms of network lifetime,
energy consumption and end-to-end delay, while keeping
the delivery r atio almost similar to the compared routing
protocol.
Acknowledgment
This work was supported by Defense Acquisition Program
Administration and Agency for Defense Development under
the contrac t UD100002KD. This paper is an extension of our
conference paper appeared in AST-2011 [13].
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